Abstract: Techniques for detecting abnormal audio events in a given environment, including learning the modeling of the environment to be surveilled during which a database is created by extraction of acoustic parameters associated with audio streams picked up over a fixed time period and an unsupervized automatic segmentation of said streams, followed by grouping the segments in classes and a statistical modeling of the segment classes, a usage phase including analysis of an audio stream, with the extraction of the acoustic parameters, automatic segmentation of said analyzed stream substantially identical to that used during the learning phase and determining a likelihood of each statistical model contained in the database for each of the segments of the analyzed audio stream, resulting in a likelihood value which is compared to a threshold value to determine the presence or absence of audio anomalies in the analyzed audio stream.

Abstract: A method and system for detecting abnormal events in a given environment comprises a model construction step comprising: a) a step of unsupervised initialization of Q groups; b) a step of definition of a model of normality consisting of 1-class SVM classifiers; c) a step of optimum distribution of the audio signals in the Q different groups; d) repetition of the steps b and c until a stop criterion C1, is checked and a model M is obtained; and a step of use of the model(s) M obtained from the construction step comprising the analysis of an unknown audio signal ST assigning a score to a 1-class SVM classifier, and a comparison of all the scores fq obtained using decision rules in order to determine the presence or absence of an anomaly in the audio signal analyzed.

Abstract: Techniques for detecting abnormal audio events in a given environment, including learning the modeling of the environment to be surveilled during which a database is created by extraction of acoustic parameters associated with audio streams picked up over a fixed time period and an unsupervised automatic segmentation of said streams, followed by grouping the segments in classes and a statistical modeling of the segment classes, a usage phase including analysis of an audio stream, with the extraction of the acoustic parameters, automatic segmentation of said analysed stream substantially identical to that used during the learning phase and determining a likelihood of each statistical model contained in the database for each of the segments of the analysed audio stream, resulting in a likelihood value which is compared to a threshold value to determine the presence or absence of audio anomalies in the analysed audio stream.

Abstract: A method and interactive system for the on-line transmission of a high-resolution video sequence composed of a succession of T images includes a step of selecting relevant images comprising at least the following steps: split each image to be transmitted at the instant t into a number N of zones, for each zone n determined in the previous step, calculate a value representative of the content of said zone, for each image to be transmitted, generate a vector representative of the content of said image containing the values obtained in the previous step, calculate a normalized coefficient of correlation ? between the reference vector determined for a previously selected image and that calculated for the current image, make a decision on the selection (or not) of the current image as a function of the value of the normalized correlation coefficient ?.

Abstract: Method of coding and decoding speech for voice communications using a vocoder with very low bit rate comprising an analysis part for the coding and the transmission of the parameters of the speech signal, such as the voicing information per sub-band, the pitch, the gains, the LSF spectral parameters and a synthesis part for the reception and the decoding of the parameters transmitted and the reconstruction of the speech signal comprising at least the following steps: grouping together the voicing parameters, pitch, gains, LSF coefficients over N consecutive frames to form a superframe, performing a vector quantization of the voicing information in the course of each superframe by formulating a classification using the information on the chaining in terms of voicing existing over 2 consecutive elementary frames, the voicing information makes it possible specifically to identify classes of sounds for which the allocation of the bit rate and the associated dictionaries will be optimized, coding the pitch, the ga

Abstract: A method for the selection of synthesis units of a piece of information that can be decomposed into synthesis units, comprises at least the following steps for a considered information segment: determining the mean fundamental frequency value F0 for the information segment considered; selecting a sub-set of synthesis units defined as being the sub-set whose mean pitch values are the closest to the pitch value F0; applying one or more proximity criteria to the selected synthesis units to determine a synthesis unit representing the information segment.